15 citations · 44 across the 5 of their papers we have counts for
6 papers
Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information
Yi Zeng, Minzhou Pan, Hoang Anh Just +3
Backdoor attacks insert malicious data into a training set so that, during inference time, it misclassifies inputs that have been patched with a backdoor trigger as the malware spe…
A Unified Framework for Task-Driven Data Quality Management
Tianhao Wang, Yi Zeng, Ming Jin +1
High-quality data is critical to train performant Machine Learning (ML) models, highlighting the importance of Data Quality Management (DQM). Existing DQM schemes often cannot sati…
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques
Han Qiu, Yi Zeng, Tianwei Zhang +2
It is extensively studied that Deep Neural Networks (DNNs) are vulnerable to Adversarial Examples (AEs). With more and more advanced adversarial attack methods have been developed,…
A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networks
Yi Zeng, Han Qiu, Gerard Memmi +1
Deep Neural Networks (DNNs) in Computer Vision (CV) are well-known to be vulnerable to Adversarial Examples (AEs), namely imperceptible perturbations added maliciously to cause wro…
Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques
Han Qiu, Yi Zeng, Qinkai Zheng +3
Deep Neural Networks (DNNs) are well-known to be vulnerable to Adversarial Examples (AEs). A large amount of efforts have been spent to launch and heat the arms race between the at…
TEST: an End-to-End Network Traffic Examination and Identification Framework Based on Spatio-Temporal Features Extraction
Yi Zeng, Zihao Qi, Wencheng Chen +3
With more encrypted network traffic gets involved in the Internet, how to effectively identify network traffic has become a top priority in the field. Accurate identification of th…